In this project, I conduct data analysis and visualization on different variables, including age, gender, marital status and country. Then I find that country is the variable I am most interested in (since there are so many countries in this data set and each country corresponds to various answers). I decide to study on the relaionships among country, original_hm and reflection_period. Finally, I discover that there could be some hidden patterns for the answers after spending time exploring them on different countries.
First, I work on the processed_moments data set with R Shiny to get a general idea about the answers of happy moments.
library(tidyverse)
library(tidytext)
library(DT)
library(scales)
library(wordcloud2)
library(gridExtra)
library(ngram)
library(shiny)
library(ggplot2)
library(reshape2)
library(lemon)We use the processed data for our analysis and combine it with the demographic information available.
hm_data <- read_csv("../output/processed_moments.csv")
urlfile<-'https://raw.githubusercontent.com/rit-public/HappyDB/master/happydb/data/demographic.csv'
demo_data <- read_csv(urlfile)hm_data <- hm_data %>%
inner_join(demo_data, by = "wid") %>%
select(wid,
original_hm,
gender,
marital,
parenthood,
reflection_period,
age,
country,
ground_truth_category,
text) %>%
mutate(count = sapply(hm_data$text, wordcount)) %>%
filter(gender %in% c("m", "f")) %>%
filter(marital %in% c("single", "married")) %>%
filter(parenthood %in% c("n", "y")) %>%
filter(reflection_period %in% c("24h", "3m")) %>%
mutate(reflection_period = fct_recode(reflection_period,
months_3 = "3m", hours_24 = "24h"))datatable(hm_data)## Warning in instance$preRenderHook(instance): It seems your data is too
## big for client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html